Autism Spectrum Disorder (ASD) is characterized by impairments in social communication and restricted or repetitive behavior or interests. The application of genomic technologies has led to the identification of many of the genes underlying ASD, presenting the opportunity to assess the insight these risk genes can give into the etiology of ASD. In this proposal we aim to: 1) Generate a list of ASD-associated genes; 2) Identify points of convergence between these genes in biological data (e.g. gene regulation and expression); and 3) Validate these points of convergence in model systems. Since ASD is a human neurodevelopmental disorder we will prioritize biological data that is collected longitudinally across development from human brain tissue. In our prior work we have demonstrated that de novo mutations, specifically copy number variants (CNVs) and loss of function (LoF) point mutations, are strongly associated with ASD. Furthermore, these mutations cluster at ASD risk genes and loci in cases but not in controls. By comparing the distribution of these mutations between cases and controls we can identify the points of mutational clustering that represent ASD risk loci (e.g. CNVs at the 500kbp 16p11.2 locus and LoFs at the gene CHD8). We have developed a statistical framework to assess this clustering as well as incorporating evidence from inherited variants and case-control data. This framework is called the Transmitted and De novo Associated Test (TADA). In Aim 1 we will develop this test further to incorporate all the available CNV, exome, genome, and targeted sequencing data into a single ASD gene list, ranked by the degree of ASD association. Previously we used the top nine ASD risk genes as seeds for gene co-expression networks and assessed the validity of these networks by their ability to incorporate 120 independent ASD risk genes. By limiting the co- expression input data to narrow windows of development and specific brain regions we could identify the spatiotemporal networks with the greatest enrichment, for example pre-frontal cortex in mid-fetal development. In Aim 2, we propose a similar approach, but using the DAWN (Detecting Association With Networks) method developed by our group. DAWN uses the narrow windows of co-expression data as before, but is able to incorporate evidence from other datasets such as gene regulation, and protein-protein interaction (PPI). By seeding the DAWN networks with the highest confidence genes we will assess the spatiotemporal networks that best predict other ASD genes. ASD shows a significant sex bias implicating an interaction between ASD etiology and sexually dimorphic factors. Building on our work of identifying sexually dimorphic transcripts in the developing human brain we will test their enrichment within specific networks identified by DAWN. To validate the ASD-associated networks, in Aim 3 we will identify the gene that best represents each network and assess if disrupting it also disrupts the other genes within the network. We will disrupt each gene using CRISPR/Cas9 in both mice and human-derived iPSCs and assess the genes disrupted using RNA-Seq.